Optical network security management: requirements, architecture, and efficient machine learning models for detection of evolving threats [Invited]

As the communication infrastructure that sustains critical societal services, optical networks need to function in a secure and agile way. Thus, cognitive and automated security management functionalities are needed, fueled by the proliferating machine learning (ML) techniques and compatible with co...

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Bibliographic Details
Published in:Journal of optical communications and networking 2021-02, Vol.13 (2), p.A144-A155
Main Authors: Furdek, Marija, Natalino, Carlos, Di Giglio, Andrea, Schiano, Marco
Format: Article
Language:English
Subjects:
Adaptive optics
Automation
Communication networks
Data models
Diagnostic systems
Dimensionality reduction
Economic and social effects
Efficiency
Fiber optic networks
Infrastructure as a service (IaaS)
Learning systems
Machine learning
Monitoring
Network architecture
Network control
Network security
Optical communication
Optical fiber networks
Optical polarization
Security
Security management
Semi-supervised learning
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Summary:As the communication infrastructure that sustains critical societal services, optical networks need to function in a secure and agile way. Thus, cognitive and automated security management functionalities are needed, fueled by the proliferating machine learning (ML) techniques and compatible with common network control entities and procedures. Automated management of optical network security requires advancements both in terms of the performance and efficiency of ML approaches for security diagnostics, as well as novel management architectures and functionalities. This paper tackles these challenges by proposing what we believe to be a novel functional block called the security operation center, describing its architecture, specifying key requirements on the supported functionalities, and providing guidelines on its integration with optical-layer controller. Moreover, to boost efficiency of ML-based security diagnostic techniques when processing high-dimensional optical performance monitoring data in the presence of previously unseen physical-layer attacks, we combine unsupervised and semi-supervised learning techniques with three different dimensionality reduction methods and analyze the resulting performance and trade-offs between the ML accuracy and run-time complexity.
ISSN:1943-0620
1943-0639
1943-0639
DOI:10.1364/JOCN.402884